MinCall - MinION end2end convolutional deep learning basecaller
This addresses the error problem for portable DNA sequencing users, representing a strong incremental improvement in basecalling accuracy.
The researchers tackled the high error rate of Oxford Nanopore's MinION DNA sequencer by developing MinCall, an end-to-end convolutional deep learning basecaller, which achieved a 91.4% median match rate on an E. Coli dataset.
The Oxford Nanopore Technologies's MinION is the first portable DNA sequencing device. It is capable of producing long reads, over 100 kBp were reported. However, it has significantly higher error rate than other methods. In this study, we present MinCall, an end2end basecaller model for the MinION. The model is based on deep learning and uses convolutional neural networks (CNN) in its implementation. For extra performance, it uses cutting edge deep learning techniques and architectures, batch normalization and Connectionist Temporal Classification (CTC) loss. The best performing deep learning model achieves 91.4% median match rate on E. Coli dataset using R9 pore chemistry and 1D reads.